Is it compulsary to normalize the dataset if doing so can negatively impact a Binary Logistic regression performance?
I found the answer to my question, went back to the Python script and in the command that fits the model i.e.

 LR = LogisticRegression (C=0.1, solver = "sag",max_iter=1000).fit (X_train, y_train)


The parameter C was set to 0.001 which is a very small value (meaning lambda is very high as C=1/lambda) (C is the regularization strength and smaller values indicate stronger regularization). more on that matter can be found here and here